Automatic link establishment

Abstract: When Hurricane Katrina hit the Gulf Coast of the United States in 2005, it severely damaged the telecommunications infrastructure, isolating the area from the outside world. With all the high-end emergency communication gear in its path being knocked out by the hurricane, highfrequency (HF) amateur radio systems played a critical role in rescue and recovery operations. Such tragic incidents have resulted in re-appreciation of the "good old" HF technology that has been an essential part of worldwide information transmission since the advent of radio. HF systems traditionally have been associated with analog voice and very-low-rate data transmission. With the shift from analog to digital in voice communication and increasing demands for high-rate data transmission (e.g., email, Internet, FTP), HF communication has been going through a renaissance. Innovative techniques are required to push the capacity limits of the HF band. Our tutorial provides a contemporary overview of HF communication and discusses cooperative diversity as an enabler to support the challenging expectations of future-generation HF communication systems.

Abstract: The high-frequency(HF) communication is one of essential communication methods for military and emergency application However, the selection of communication frequency channel is always a difficult problem as the crowded spectrum, the time-varying channels, and the malicious intelligent jamming The existing frequency hopping, automatic link establishment and some new anti-jamming technologies can not completely solve the above problems In this article, we adopt deep reinforcement learning to solve this intractable challenge First, the combination of the spectrum state and the channel gain state is defined as the complex environmental state, and the Markov characteristic of defined state is analyzed and proved Then, considering that the spectrum state and channel gain state are heterogeneous information, a new deep Q network (DQN) framework is designed, which contains multiple sub-networks to process different kinds of information Finally, aiming to improve the learning speed and efficiency, the optimization targets of corresponding sub-networks are reasonably designed, and a heterogeneous information fusion deep reinforcement learning (HIF-DRL) algorithm is designed for the specific frequency selection Simulation results show that the proposed algorithm performs well in channel prediction, jamming avoidance and frequency channel selection

Abstract: High frequency (HF) communication is widely spread due to some merits like easy deployment and wide communication coverage. Spectrum prediction is a promising technique to facilitate the working frequency selection and enhance the function of automatic link establishment. Most of the existing spectrum prediction algorithms focus on predicting spectrum values in a slot-by-slot manner and therefore are lack of timeliness. Deep learning based spectrum prediction is developed in this paper by simultaneously predicting multi-slot ahead states of multiple spectrum points within a period of time. Specifically, we first employ supervised learning and construct samples depending on long-term and short-term HF spectrum data. Then, advanced residual units are introduced to build multiple residual network modules to respectively capture characteristics in these data with diverse time scales. Further, convolution neural network fuses the outputs of residual network modules above for temporal-spectral prediction, which is combined with residual network modules to construct the deep temporal-spectral residual network. Experiments have demonstrated that the approach proposed in this paper has a significant advantage over the benchmark schemes.

Abstract: Automatic Link Establishment (ALE) is the mechanism through which automated HF radio systems automatically establish communication on one of a set of channels where a channel is typically a 3 kHz single sideband frequency assignment. In some cases this may be the "best" channel based on either predicted or measured parameters. In other cases the selected channel may simply be good enough to establish communication at some minimum level of service. This paper provides a basic overview of the basic features of both second and third generation ALE system and then discusses what improvements and enhancements may be possible in a next generation ALE system.